Maintaining appropriate anesthesia during surgical operation is difficult. Clinicians providing anesthesia to a patient have to continuously respond to changes in the patient's condition and, when needed, take emergency actions. One key to managing anesthesia is maintaining an appropriate sedation level for a patient, which requires accurate assessment of a patient's sedation level and an understanding of how that particular patient will respond to the administered anesthetic drugs.
Prior art anesthesia care and monitoring has focused on responsive data—determining a patient's current sedation level as a result of anesthesia that has already been administered. For example, the common indicator for sedation level is depth of anesthesia monitoring, such as State Entropy (SE) or Bispectral Index (BIS). However, depth of anesthesia monitoring only provides responsive data, information about the effect that previously administered anesthesia has already caused. Such monitoring does not provide proactive information regarding the patient's future state. Depth of anesthesia monitoring is important; however, anesthesia care would be significantly improved if clinicians could predict a patient's future state, such as how a patient will respond to administration of an anesthetic drug. It is desirable for clinicians to be able to predict patient response, and to be able to apply that prediction into the monitoring and anesthetic maintenance for patients.
Point of care modeling, which is modeling patient state during operation to aid clinical decision making, is a new and promising information source in anesthetics that aims to provide an indicator for patient sedation level. Population models are available that offer information about average patient responses to anesthetic drugs for a particular demographic or population. Typically effect site concentrations predicted by a drug model are presented in population scale. However, when predicting the sedation level according to population models, the result may not be accurate for a single patient. Population data is not patient-specific and often provides information that varies dramatically from data gathered by depth of anesthesia monitoring. Thus, population models are not a reliable source on which a clinician can depend to predict a patient's drug reaction, and actually have the potential to lead clinicians astray. The difference between the population model results and the depth of anesthesia monitoring can increase cognitive load to clinicians by adding yet another parameter to track.
Accurate anesthetic drug administration, for example during a surgical procedure, is extremely important because incorrect dosing can have large consequences; under dosing may lead to patient waking up during operation and overdosing can cause hemodynamic instability. Further, high sensitivity to anesthetics is a known risk factor when administering anesthesia. Because clinicians are unable to predict in advance what a patient's sensitivity level will be, they cannot predict what the sedation level will be. Since anesthetic drugs may take long time to take effect, clinicians are often stuck trying to react to situations of overdosing or under dosing. Thus, a parameter is needed that allows a clinician to better estimate the risks of anesthesia and to make more informed decisions about courses of anesthesia administration.